Overview

Dataset statistics

Number of variables23
Number of observations4080
Missing cells23419
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory733.2 KiB
Average record size in memory184.0 B

Variable types

Numeric15
Categorical8

Warnings

slpeffp is highly correlated with slpprdp and 1 other fieldsHigh correlation
slpprdp is highly correlated with slpeffpHigh correlation
times34p is highly correlated with timest2pHigh correlation
timest2p is highly correlated with times34pHigh correlation
waso is highly correlated with slpeffpHigh correlation
rdi3p is highly correlated with ai_all and 1 other fieldsHigh correlation
ai_all is highly correlated with rdi3pHigh correlation
avgsat is highly correlated with minsatHigh correlation
minsat is highly correlated with rdi3p and 1 other fieldsHigh correlation
saqli is highly correlated with fosqHigh correlation
fosq is highly correlated with saqliHigh correlation
sh308a is highly correlated with sh308bHigh correlation
sh308b is highly correlated with sh308a and 1 other fieldsHigh correlation
sh308c is highly correlated with sh308bHigh correlation
sh308d is highly correlated with sh308eHigh correlation
sh308e is highly correlated with sh308dHigh correlation
slpeffp is highly correlated with slpprdp and 1 other fieldsHigh correlation
slpprdp is highly correlated with slpeffpHigh correlation
times34p is highly correlated with timest2pHigh correlation
timest2p is highly correlated with times34pHigh correlation
waso is highly correlated with slpeffpHigh correlation
rdi3p is highly correlated with minsatHigh correlation
avgsat is highly correlated with minsatHigh correlation
minsat is highly correlated with rdi3p and 1 other fieldsHigh correlation
saqli is highly correlated with fosqHigh correlation
fosq is highly correlated with saqliHigh correlation
sh308a is highly correlated with sh308bHigh correlation
sh308b is highly correlated with sh308a and 1 other fieldsHigh correlation
sh308c is highly correlated with sh308bHigh correlation
sh308d is highly correlated with sh308eHigh correlation
sh308e is highly correlated with sh308dHigh correlation
supinep is highly correlated with raceHigh correlation
slpeffp is highly correlated with waso and 1 other fieldsHigh correlation
slpprdp is highly correlated with raceHigh correlation
timeremp is highly correlated with raceHigh correlation
times34p is highly correlated with timest2p and 1 other fieldsHigh correlation
timest1p is highly correlated with gender and 1 other fieldsHigh correlation
timest2p is highly correlated with times34p and 2 other fieldsHigh correlation
waso is highly correlated with slpeffp and 2 other fieldsHigh correlation
rdi3p is highly correlated with minsat and 2 other fieldsHigh correlation
ai_all is highly correlated with gender and 1 other fieldsHigh correlation
avgsat is highly correlated with raceHigh correlation
minsat is highly correlated with rdi3p and 1 other fieldsHigh correlation
gender is highly correlated with timest1p and 5 other fieldsHigh correlation
race is highly correlated with supinep and 12 other fieldsHigh correlation
saqli is highly correlated with sh308d and 2 other fieldsHigh correlation
fosq is highly correlated with sh308d and 1 other fieldsHigh correlation
sh308d is highly correlated with saqli and 1 other fieldsHigh correlation
sh308e is highly correlated with saqli and 1 other fieldsHigh correlation
sh308f is highly correlated with saqliHigh correlation
timeremp is highly correlated with timest2pHigh correlation
slpprdp is highly correlated with slpeffpHigh correlation
minsat is highly correlated with avgsatHigh correlation
sh308b is highly correlated with sh308c and 2 other fieldsHigh correlation
avgsat is highly correlated with minsatHigh correlation
rdi3p is highly correlated with ai_allHigh correlation
ai_all is highly correlated with rdi3pHigh correlation
waso is highly correlated with slpeffpHigh correlation
times34p is highly correlated with gender and 1 other fieldsHigh correlation
gender is highly correlated with times34pHigh correlation
sh308e is highly correlated with fosq and 3 other fieldsHigh correlation
sh308c is highly correlated with sh308b and 2 other fieldsHigh correlation
fosq is highly correlated with sh308e and 2 other fieldsHigh correlation
timest2p is highly correlated with timeremp and 1 other fieldsHigh correlation
timest1p is highly correlated with slpeffpHigh correlation
sh308a is highly correlated with sh308b and 2 other fieldsHigh correlation
slpeffp is highly correlated with slpprdp and 2 other fieldsHigh correlation
sh308f is highly correlated with sh308b and 4 other fieldsHigh correlation
saqli is highly correlated with sh308e and 2 other fieldsHigh correlation
sh308d is highly correlated with sh308e and 3 other fieldsHigh correlation
supinep has 1442 (35.3%) missing values Missing
slpeffp has 1429 (35.0%) missing values Missing
slpprdp has 1429 (35.0%) missing values Missing
timeremp has 1460 (35.8%) missing values Missing
times34p has 1460 (35.8%) missing values Missing
timest1p has 1460 (35.8%) missing values Missing
timest2p has 1460 (35.8%) missing values Missing
waso has 1429 (35.0%) missing values Missing
rdi3p has 1429 (35.0%) missing values Missing
ai_all has 1467 (36.0%) missing values Missing
avgsat has 1429 (35.0%) missing values Missing
minsat has 1429 (35.0%) missing values Missing
saqli has 2890 (70.8%) missing values Missing
fosq has 2895 (71.0%) missing values Missing
sh308a has 46 (1.1%) missing values Missing
sh308b has 47 (1.2%) missing values Missing
sh308c has 50 (1.2%) missing values Missing
sh308d has 57 (1.4%) missing values Missing
sh308e has 51 (1.2%) missing values Missing
sh308f has 60 (1.5%) missing values Missing
supinep has 344 (8.4%) zeros Zeros
times34p has 122 (3.0%) zeros Zeros

Reproduction

Analysis started2021-09-08 00:35:06.904239
Analysis finished2021-09-08 00:35:36.088094
Duration29.18 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

supinep
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct102
Distinct (%)3.9%
Missing1442
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean35.37604246
Minimum0
Maximum101
Zeros344
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:36.184402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median29
Q357
95-th percentile97
Maximum101
Range101
Interquartile range (IQR)48

Descriptive statistics

Standard deviation30.2725639
Coefficient of variation (CV)0.8557363062
Kurtosis-0.7091130449
Mean35.37604246
Median Absolute Deviation (MAD)23
Skewness0.6249746412
Sum93322
Variance916.4281251
MonotonicityNot monotonic
2021-09-07T20:35:36.307342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0344
 
8.4%
100107
 
2.6%
160
 
1.5%
245
 
1.1%
1940
 
1.0%
2040
 
1.0%
1540
 
1.0%
1239
 
1.0%
1339
 
1.0%
1038
 
0.9%
Other values (92)1846
45.2%
(Missing)1442
35.3%
ValueCountFrequency (%)
0344
8.4%
160
 
1.5%
245
 
1.1%
334
 
0.8%
434
 
0.8%
530
 
0.7%
632
 
0.8%
731
 
0.8%
833
 
0.8%
934
 
0.8%
ValueCountFrequency (%)
10110
 
0.2%
100107
2.6%
9910
 
0.2%
984
 
0.1%
9712
 
0.3%
965
 
0.1%
9510
 
0.2%
946
 
0.1%
937
 
0.2%
9212
 
0.3%

slpeffp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct499
Distinct (%)18.8%
Missing1429
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean79.17057714
Minimum24.4
Maximum98.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:36.427387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile56.4
Q173.2
median81.5
Q387.6
95-th percentile93.5
Maximum98.8
Range74.4
Interquartile range (IQR)14.4

Descriptive statistics

Standard deviation11.64327784
Coefficient of variation (CV)0.1470657189
Kurtosis1.755317017
Mean79.17057714
Median Absolute Deviation (MAD)6.9
Skewness-1.193223641
Sum209881.2
Variance135.5659189
MonotonicityNot monotonic
2021-09-07T20:35:36.552121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.422
 
0.5%
80.218
 
0.4%
87.417
 
0.4%
85.617
 
0.4%
86.717
 
0.4%
78.616
 
0.4%
82.416
 
0.4%
84.915
 
0.4%
85.215
 
0.4%
86.215
 
0.4%
Other values (489)2483
60.9%
(Missing)1429
35.0%
ValueCountFrequency (%)
24.41
< 0.1%
25.51
< 0.1%
26.91
< 0.1%
27.91
< 0.1%
30.41
< 0.1%
30.71
< 0.1%
31.11
< 0.1%
33.41
< 0.1%
33.71
< 0.1%
34.21
< 0.1%
ValueCountFrequency (%)
98.81
 
< 0.1%
97.71
 
< 0.1%
97.63
0.1%
97.41
 
< 0.1%
97.31
 
< 0.1%
972
< 0.1%
96.91
 
< 0.1%
96.81
 
< 0.1%
96.71
 
< 0.1%
96.64
0.1%

slpprdp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct359
Distinct (%)13.5%
Missing1429
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean374.0637495
Minimum68
Maximum605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:36.688932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile249
Q1336
median381
Q3417
95-th percentile480
Maximum605
Range537
Interquartile range (IQR)81

Descriptive statistics

Standard deviation69.36444695
Coefficient of variation (CV)0.1854348277
Kurtosis0.8719978267
Mean374.0637495
Median Absolute Deviation (MAD)40
Skewness-0.5201543567
Sum991643
Variance4811.426501
MonotonicityNot monotonic
2021-09-07T20:35:36.814946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38432
 
0.8%
40229
 
0.7%
37226
 
0.6%
40424
 
0.6%
41723
 
0.6%
38722
 
0.5%
39422
 
0.5%
37321
 
0.5%
38021
 
0.5%
39921
 
0.5%
Other values (349)2410
59.1%
(Missing)1429
35.0%
ValueCountFrequency (%)
681
< 0.1%
981
< 0.1%
1082
< 0.1%
1121
< 0.1%
1161
< 0.1%
1171
< 0.1%
1331
< 0.1%
1381
< 0.1%
1431
< 0.1%
1471
< 0.1%
ValueCountFrequency (%)
6051
< 0.1%
5831
< 0.1%
5631
< 0.1%
5521
< 0.1%
5511
< 0.1%
5501
< 0.1%
5441
< 0.1%
5421
< 0.1%
5411
< 0.1%
5401
< 0.1%

timeremp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)1.7%
Missing1460
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean20.73549618
Minimum0
Maximum48
Zeros13
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:36.931640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q117
median21
Q325
95-th percentile31
Maximum48
Range48
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.466340105
Coefficient of variation (CV)0.3118488243
Kurtosis0.6807289508
Mean20.73549618
Median Absolute Deviation (MAD)4
Skewness-0.1575034734
Sum54327
Variance41.81355435
MonotonicityNot monotonic
2021-09-07T20:35:37.052053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
21178
 
4.4%
23172
 
4.2%
22168
 
4.1%
24167
 
4.1%
20163
 
4.0%
18157
 
3.8%
19154
 
3.8%
17141
 
3.5%
25141
 
3.5%
26116
 
2.8%
Other values (35)1063
26.1%
(Missing)1460
35.8%
ValueCountFrequency (%)
013
0.3%
12
 
< 0.1%
26
 
0.1%
38
 
0.2%
44
 
0.1%
510
 
0.2%
612
0.3%
723
0.6%
817
0.4%
929
0.7%
ValueCountFrequency (%)
481
 
< 0.1%
471
 
< 0.1%
423
 
0.1%
412
 
< 0.1%
403
 
0.1%
394
0.1%
384
0.1%
378
0.2%
368
0.2%
357
0.2%

times34p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct58
Distinct (%)2.2%
Missing1460
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean15.94503817
Minimum0
Maximum59
Zeros122
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:37.171127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median15
Q323
95-th percentile36
Maximum59
Range59
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.95616044
Coefficient of variation (CV)0.6871203644
Kurtosis0.09104725357
Mean15.94503817
Median Absolute Deviation (MAD)8
Skewness0.6000274144
Sum41776
Variance120.0374515
MonotonicityNot monotonic
2021-09-07T20:35:37.288023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0122
 
3.0%
16101
 
2.5%
1598
 
2.4%
1997
 
2.4%
196
 
2.4%
390
 
2.2%
1887
 
2.1%
1786
 
2.1%
1386
 
2.1%
1485
 
2.1%
Other values (48)1672
41.0%
(Missing)1460
35.8%
ValueCountFrequency (%)
0122
3.0%
196
2.4%
284
2.1%
390
2.2%
481
2.0%
575
1.8%
676
1.9%
784
2.1%
864
1.6%
972
1.8%
ValueCountFrequency (%)
592
< 0.1%
581
 
< 0.1%
561
 
< 0.1%
552
< 0.1%
532
< 0.1%
521
 
< 0.1%
513
0.1%
502
< 0.1%
492
< 0.1%
481
 
< 0.1%

timest1p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)1.2%
Missing1460
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean5.755725191
Minimum0
Maximum38
Zeros12
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:37.401313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile14
Maximum38
Range38
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.999983969
Coefficient of variation (CV)0.694957427
Kurtosis6.619406008
Mean5.755725191
Median Absolute Deviation (MAD)2
Skewness1.988752181
Sum15080
Variance15.99987175
MonotonicityNot monotonic
2021-09-07T20:35:37.504622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3379
 
9.3%
4364
 
8.9%
5348
 
8.5%
2302
 
7.4%
6263
 
6.4%
7210
 
5.1%
8148
 
3.6%
1131
 
3.2%
9113
 
2.8%
1082
 
2.0%
Other values (21)280
 
6.9%
(Missing)1460
35.8%
ValueCountFrequency (%)
012
 
0.3%
1131
 
3.2%
2302
7.4%
3379
9.3%
4364
8.9%
5348
8.5%
6263
6.4%
7210
5.1%
8148
 
3.6%
9113
 
2.8%
ValueCountFrequency (%)
381
 
< 0.1%
331
 
< 0.1%
301
 
< 0.1%
291
 
< 0.1%
283
0.1%
253
0.1%
243
0.1%
233
0.1%
226
0.1%
215
0.1%

timest2p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct71
Distinct (%)2.7%
Missing1460
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean57.71183206
Minimum20
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:37.627801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile39
Q150
median58
Q366
95-th percentile75
Maximum98
Range78
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.05143366
Coefficient of variation (CV)0.1914933779
Kurtosis-0.06402875397
Mean57.71183206
Median Absolute Deviation (MAD)8
Skewness-0.07911462411
Sum151205
Variance122.134186
MonotonicityNot monotonic
2021-09-07T20:35:37.760429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62101
 
2.5%
5797
 
2.4%
5597
 
2.4%
6190
 
2.2%
5687
 
2.1%
6787
 
2.1%
5986
 
2.1%
6086
 
2.1%
5486
 
2.1%
6383
 
2.0%
Other values (61)1720
42.2%
(Missing)1460
35.8%
ValueCountFrequency (%)
201
 
< 0.1%
232
 
< 0.1%
241
 
< 0.1%
251
 
< 0.1%
264
0.1%
284
0.1%
294
0.1%
303
0.1%
316
0.1%
324
0.1%
ValueCountFrequency (%)
981
 
< 0.1%
941
 
< 0.1%
931
 
< 0.1%
921
 
< 0.1%
891
 
< 0.1%
882
< 0.1%
874
0.1%
864
0.1%
854
0.1%
844
0.1%

waso
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct262
Distinct (%)9.9%
Missing1429
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean80.19113542
Minimum2
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:37.905697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q139
median68
Q3107
95-th percentile189
Maximum378
Range376
Interquartile range (IQR)68

Descriptive statistics

Standard deviation54.75985179
Coefficient of variation (CV)0.6828666474
Kurtosis2.935467395
Mean80.19113542
Median Absolute Deviation (MAD)32
Skewness1.46244969
Sum212586.7
Variance2998.641368
MonotonicityNot monotonic
2021-09-07T20:35:38.036062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4934
 
0.8%
3833
 
0.8%
3432
 
0.8%
2432
 
0.8%
3932
 
0.8%
3232
 
0.8%
2631
 
0.8%
6530
 
0.7%
4229
 
0.7%
7028
 
0.7%
Other values (252)2338
57.3%
(Missing)1429
35.0%
ValueCountFrequency (%)
21
 
< 0.1%
42
 
< 0.1%
54
 
0.1%
62
 
< 0.1%
74
 
0.1%
87
0.2%
94
 
0.1%
105
0.1%
116
0.1%
1211
0.3%
ValueCountFrequency (%)
3781
< 0.1%
3751
< 0.1%
3711
< 0.1%
3551
< 0.1%
3521
< 0.1%
3351
< 0.1%
3301
< 0.1%
3211
< 0.1%
3171
< 0.1%
3031
< 0.1%

rdi3p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1815
Distinct (%)68.5%
Missing1429
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean15.91666541
Minimum0
Maximum117.6
Zeros11
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:38.170601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.05
Q15.015
median10.89
Q321.465
95-th percentile48.055
Maximum117.6
Range117.6
Interquartile range (IQR)16.45

Descriptive statistics

Standard deviation15.6021567
Coefficient of variation (CV)0.980240289
Kurtosis4.065626194
Mean15.91666541
Median Absolute Deviation (MAD)7.16
Skewness1.83229892
Sum42195.08
Variance243.4272937
MonotonicityNot monotonic
2021-09-07T20:35:38.298785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.3%
1.586
 
0.1%
6.036
 
0.1%
3.325
 
0.1%
0.525
 
0.1%
4.425
 
0.1%
7.545
 
0.1%
10.455
 
0.1%
4.925
 
0.1%
7.925
 
0.1%
Other values (1805)2593
63.6%
(Missing)1429
35.0%
ValueCountFrequency (%)
011
0.3%
0.121
 
< 0.1%
0.131
 
< 0.1%
0.142
 
< 0.1%
0.152
 
< 0.1%
0.162
 
< 0.1%
0.174
 
0.1%
0.221
 
< 0.1%
0.231
 
< 0.1%
0.293
 
0.1%
ValueCountFrequency (%)
117.61
< 0.1%
107.481
< 0.1%
90.851
< 0.1%
90.461
< 0.1%
88.911
< 0.1%
86.831
< 0.1%
86.111
< 0.1%
85.031
< 0.1%
84.061
< 0.1%
841
< 0.1%

ai_all
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1685
Distinct (%)64.5%
Missing1467
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean18.36719097
Minimum2.6
Maximum81.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:38.428057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile6.746
Q111.43
median16.19
Q322.65
95-th percentile36.652
Maximum81.38
Range78.78
Interquartile range (IQR)11.22

Descriptive statistics

Standard deviation10.15274789
Coefficient of variation (CV)0.5527654126
Kurtosis5.413080685
Mean18.36719097
Median Absolute Deviation (MAD)5.4
Skewness1.81965625
Sum47993.47
Variance103.0782898
MonotonicityNot monotonic
2021-09-07T20:35:38.550965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1512
 
0.3%
126
 
0.1%
13.566
 
0.1%
306
 
0.1%
14.816
 
0.1%
206
 
0.1%
12.056
 
0.1%
17.585
 
0.1%
10.675
 
0.1%
11.825
 
0.1%
Other values (1675)2550
62.5%
(Missing)1467
36.0%
ValueCountFrequency (%)
2.61
 
< 0.1%
2.881
 
< 0.1%
3.151
 
< 0.1%
3.261
 
< 0.1%
3.381
 
< 0.1%
3.41
 
< 0.1%
3.461
 
< 0.1%
3.481
 
< 0.1%
3.611
 
< 0.1%
3.663
0.1%
ValueCountFrequency (%)
81.381
< 0.1%
80.591
< 0.1%
80.561
< 0.1%
78.41
< 0.1%
75.321
< 0.1%
74.821
< 0.1%
711
< 0.1%
69.951
< 0.1%
68.821
< 0.1%
66.931
< 0.1%

avgsat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19
Distinct (%)0.7%
Missing1429
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean94.14371935
Minimum75
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:38.660515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile91
Q193
median94
Q395
95-th percentile97
Maximum100
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.955567082
Coefficient of variation (CV)0.02077214599
Kurtosis5.633713254
Mean94.14371935
Median Absolute Deviation (MAD)1
Skewness-1.178914645
Sum249575
Variance3.824242614
MonotonicityNot monotonic
2021-09-07T20:35:38.758596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
95599
14.7%
94576
14.1%
93415
 
10.2%
96394
 
9.7%
92230
 
5.6%
97176
 
4.3%
91112
 
2.7%
9054
 
1.3%
9844
 
1.1%
8918
 
0.4%
Other values (9)33
 
0.8%
(Missing)1429
35.0%
ValueCountFrequency (%)
751
 
< 0.1%
834
 
0.1%
841
 
< 0.1%
852
 
< 0.1%
861
 
< 0.1%
876
 
0.1%
8813
 
0.3%
8918
 
0.4%
9054
1.3%
91112
2.7%
ValueCountFrequency (%)
1001
 
< 0.1%
994
 
0.1%
9844
 
1.1%
97176
 
4.3%
96394
9.7%
95599
14.7%
94576
14.1%
93415
10.2%
92230
 
5.6%
91112
 
2.7%

minsat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)1.7%
Missing1429
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean84.24971709
Minimum43
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:38.861795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile73
Q182
median85
Q388
95-th percentile91
Maximum97
Range54
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.879631849
Coefficient of variation (CV)0.06978814947
Kurtosis4.433815536
Mean84.24971709
Median Absolute Deviation (MAD)3
Skewness-1.543389233
Sum223346
Variance34.57007067
MonotonicityNot monotonic
2021-09-07T20:35:38.984465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
88242
 
5.9%
86235
 
5.8%
87226
 
5.5%
89208
 
5.1%
84189
 
4.6%
90183
 
4.5%
85183
 
4.5%
83160
 
3.9%
82148
 
3.6%
91122
 
3.0%
Other values (35)755
18.5%
(Missing)1429
35.0%
ValueCountFrequency (%)
431
 
< 0.1%
481
 
< 0.1%
492
< 0.1%
501
 
< 0.1%
541
 
< 0.1%
562
< 0.1%
572
< 0.1%
602
< 0.1%
613
0.1%
622
< 0.1%
ValueCountFrequency (%)
971
 
< 0.1%
961
 
< 0.1%
954
 
0.1%
9410
 
0.2%
9334
 
0.8%
9254
 
1.3%
91122
3.0%
90183
4.5%
89208
5.1%
88242
5.9%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size231.2 KiB
2
2219 
1
1861 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
22219
54.4%
11861
45.6%

Length

2021-09-07T20:35:39.200420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:39.265513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
22219
54.4%
11861
45.6%

Most occurring characters

ValueCountFrequency (%)
22219
54.4%
11861
45.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4080
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22219
54.4%
11861
45.6%

Most occurring scripts

ValueCountFrequency (%)
Common4080
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22219
54.4%
11861
45.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22219
54.4%
11861
45.6%

race
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.2 KiB
1
3587 
2
 
256
3
 
237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4080
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13587
87.9%
2256
 
6.3%
3237
 
5.8%

Length

2021-09-07T20:35:39.440824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:39.505787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
13587
87.9%
2256
 
6.3%
3237
 
5.8%

Most occurring characters

ValueCountFrequency (%)
13587
87.9%
2256
 
6.3%
3237
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4080
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13587
87.9%
2256
 
6.3%
3237
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common4080
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13587
87.9%
2256
 
6.3%
3237
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13587
87.9%
2256
 
6.3%
3237
 
5.8%

age_s1
Real number (ℝ≥0)

Distinct52
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.1129902
Minimum39
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:39.588034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile45
Q155
median63
Q371
95-th percentile80
Maximum90
Range51
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.60183242
Coefficient of variation (CV)0.1679817797
Kurtosis-0.6875224477
Mean63.1129902
Median Absolute Deviation (MAD)8
Skewness-0.05832758207
Sum257501
Variance112.3988506
MonotonicityNot monotonic
2021-09-07T20:35:39.718273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57161
 
3.9%
58143
 
3.5%
59142
 
3.5%
74142
 
3.5%
69140
 
3.4%
62137
 
3.4%
56136
 
3.3%
61135
 
3.3%
54134
 
3.3%
55132
 
3.2%
Other values (42)2678
65.6%
ValueCountFrequency (%)
394
 
0.1%
4033
0.8%
4128
0.7%
4238
0.9%
4342
1.0%
4439
1.0%
4552
1.3%
4642
1.0%
4765
1.6%
4852
1.3%
ValueCountFrequency (%)
907
 
0.2%
893
 
0.1%
883
 
0.1%
874
 
0.1%
8610
 
0.2%
8517
0.4%
8421
0.5%
8325
0.6%
8239
1.0%
8138
0.9%

saqli
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct61
Distinct (%)5.1%
Missing2890
Missing (%)70.8%
Infinite0
Infinite (%)0.0%
Mean6.007563025
Minimum1.5
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:39.843621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile4.3915
Q15.64
median6.21
Q36.57
95-th percentile6.93
Maximum7
Range5.5
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.8256179645
Coefficient of variation (CV)0.1374297633
Kurtosis3.180302967
Mean6.007563025
Median Absolute Deviation (MAD)0.43
Skewness-1.570026868
Sum7149
Variance0.6816450234
MonotonicityNot monotonic
2021-09-07T20:35:39.958726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3668
 
1.7%
6.564
 
1.6%
6.8663
 
1.5%
6.2961
 
1.5%
6.6458
 
1.4%
6.0756
 
1.4%
6.7156
 
1.4%
6.9355
 
1.3%
6.2152
 
1.3%
6.4351
 
1.2%
Other values (51)606
 
14.9%
(Missing)2890
70.8%
ValueCountFrequency (%)
1.51
< 0.1%
1.861
< 0.1%
2.211
< 0.1%
2.361
< 0.1%
2.432
< 0.1%
2.571
< 0.1%
2.861
< 0.1%
3.072
< 0.1%
3.142
< 0.1%
3.292
< 0.1%
ValueCountFrequency (%)
720
 
0.5%
6.9355
1.3%
6.8663
1.5%
6.7936
0.9%
6.7156
1.4%
6.6458
1.4%
6.5746
1.1%
6.564
1.6%
6.4351
1.2%
6.3668
1.7%

fosq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct267
Distinct (%)22.5%
Missing2895
Missing (%)71.0%
Infinite0
Infinite (%)0.0%
Mean11.51459916
Minimum6.96
Maximum13.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.0 KiB
2021-09-07T20:35:40.079357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.96
5-th percentile9.882
Q111.17
median11.75
Q312.09
95-th percentile12.29
Maximum13.17
Range6.21
Interquartile range (IQR)0.92

Descriptive statistics

Standard deviation0.8272743194
Coefficient of variation (CV)0.07184568982
Kurtosis4.910224545
Mean11.51459916
Median Absolute Deviation (MAD)0.43
Skewness-1.88324814
Sum13644.8
Variance0.6843827995
MonotonicityNot monotonic
2021-09-07T20:35:40.194607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.18106
 
2.6%
12.2998
 
2.4%
12.0737
 
0.9%
12.1330
 
0.7%
11.9326
 
0.6%
12.0425
 
0.6%
11.8921
 
0.5%
12.0120
 
0.5%
11.9618
 
0.4%
11.8215
 
0.4%
Other values (257)789
 
19.3%
(Missing)2895
71.0%
ValueCountFrequency (%)
6.961
< 0.1%
71
< 0.1%
7.041
< 0.1%
7.431
< 0.1%
7.541
< 0.1%
7.671
< 0.1%
82
< 0.1%
8.021
< 0.1%
8.131
< 0.1%
8.251
< 0.1%
ValueCountFrequency (%)
13.171
< 0.1%
12.971
< 0.1%
12.781
< 0.1%
12.682
< 0.1%
12.671
< 0.1%
12.631
< 0.1%
12.621
< 0.1%
12.612
< 0.1%
12.61
< 0.1%
12.581
< 0.1%

sh308a
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing46
Missing (%)1.1%
Memory size238.3 KiB
2.0
1625 
3.0
1287 
1.0
550 
4.0
411 
5.0
 
161

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12102
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.01625
39.8%
3.01287
31.5%
1.0550
 
13.5%
4.0411
 
10.1%
5.0161
 
3.9%
(Missing)46
 
1.1%

Length

2021-09-07T20:35:40.434994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:40.502835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.01625
40.3%
3.01287
31.9%
1.0550
 
13.6%
4.0411
 
10.2%
5.0161
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.4034
33.3%
04034
33.3%
21625
13.4%
31287
 
10.6%
1550
 
4.5%
4411
 
3.4%
5161
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8068
66.7%
Other Punctuation4034
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04034
50.0%
21625
20.1%
31287
 
16.0%
1550
 
6.8%
4411
 
5.1%
5161
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.4034
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4034
33.3%
04034
33.3%
21625
13.4%
31287
 
10.6%
1550
 
4.5%
4411
 
3.4%
5161
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4034
33.3%
04034
33.3%
21625
13.4%
31287
 
10.6%
1550
 
4.5%
4411
 
3.4%
5161
 
1.3%

sh308b
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing47
Missing (%)1.2%
Memory size238.3 KiB
3.0
1428 
2.0
1395 
4.0
592 
1.0
433 
5.0
185 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12099
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.01428
35.0%
2.01395
34.2%
4.0592
14.5%
1.0433
 
10.6%
5.0185
 
4.5%
(Missing)47
 
1.2%

Length

2021-09-07T20:35:40.701521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:40.769990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.01428
35.4%
2.01395
34.6%
4.0592
14.7%
1.0433
 
10.7%
5.0185
 
4.6%

Most occurring characters

ValueCountFrequency (%)
.4033
33.3%
04033
33.3%
31428
 
11.8%
21395
 
11.5%
4592
 
4.9%
1433
 
3.6%
5185
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8066
66.7%
Other Punctuation4033
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04033
50.0%
31428
 
17.7%
21395
 
17.3%
4592
 
7.3%
1433
 
5.4%
5185
 
2.3%
Other Punctuation
ValueCountFrequency (%)
.4033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4033
33.3%
04033
33.3%
31428
 
11.8%
21395
 
11.5%
4592
 
4.9%
1433
 
3.6%
5185
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII12099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4033
33.3%
04033
33.3%
31428
 
11.8%
21395
 
11.5%
4592
 
4.9%
1433
 
3.6%
5185
 
1.5%

sh308c
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing50
Missing (%)1.2%
Memory size238.2 KiB
2.0
1519 
3.0
1151 
1.0
653 
4.0
545 
5.0
162 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12090
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.01519
37.2%
3.01151
28.2%
1.0653
16.0%
4.0545
 
13.4%
5.0162
 
4.0%
(Missing)50
 
1.2%

Length

2021-09-07T20:35:40.968899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:41.037244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.01519
37.7%
3.01151
28.6%
1.0653
16.2%
4.0545
 
13.5%
5.0162
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.4030
33.3%
04030
33.3%
21519
 
12.6%
31151
 
9.5%
1653
 
5.4%
4545
 
4.5%
5162
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8060
66.7%
Other Punctuation4030
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04030
50.0%
21519
 
18.8%
31151
 
14.3%
1653
 
8.1%
4545
 
6.8%
5162
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.4030
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12090
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4030
33.3%
04030
33.3%
21519
 
12.6%
31151
 
9.5%
1653
 
5.4%
4545
 
4.5%
5162
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12090
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4030
33.3%
04030
33.3%
21519
 
12.6%
31151
 
9.5%
1653
 
5.4%
4545
 
4.5%
5162
 
1.3%

sh308d
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing57
Missing (%)1.4%
Memory size238.1 KiB
2.0
1539 
3.0
1194 
1.0
699 
4.0
440 
5.0
 
151

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12069
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row5.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.01539
37.7%
3.01194
29.3%
1.0699
17.1%
4.0440
 
10.8%
5.0151
 
3.7%
(Missing)57
 
1.4%

Length

2021-09-07T20:35:41.241609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:41.309791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.01539
38.3%
3.01194
29.7%
1.0699
17.4%
4.0440
 
10.9%
5.0151
 
3.8%

Most occurring characters

ValueCountFrequency (%)
.4023
33.3%
04023
33.3%
21539
 
12.8%
31194
 
9.9%
1699
 
5.8%
4440
 
3.6%
5151
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8046
66.7%
Other Punctuation4023
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04023
50.0%
21539
 
19.1%
31194
 
14.8%
1699
 
8.7%
4440
 
5.5%
5151
 
1.9%
Other Punctuation
ValueCountFrequency (%)
.4023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12069
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4023
33.3%
04023
33.3%
21539
 
12.8%
31194
 
9.9%
1699
 
5.8%
4440
 
3.6%
5151
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4023
33.3%
04023
33.3%
21539
 
12.8%
31194
 
9.9%
1699
 
5.8%
4440
 
3.6%
5151
 
1.3%

sh308e
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing51
Missing (%)1.2%
Memory size238.2 KiB
2.0
1708 
3.0
1246 
1.0
707 
4.0
306 
5.0
 
62

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12087
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.01708
41.9%
3.01246
30.5%
1.0707
17.3%
4.0306
 
7.5%
5.062
 
1.5%
(Missing)51
 
1.2%

Length

2021-09-07T20:35:41.513025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:41.581693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.01708
42.4%
3.01246
30.9%
1.0707
17.5%
4.0306
 
7.6%
5.062
 
1.5%

Most occurring characters

ValueCountFrequency (%)
.4029
33.3%
04029
33.3%
21708
14.1%
31246
 
10.3%
1707
 
5.8%
4306
 
2.5%
562
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8058
66.7%
Other Punctuation4029
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04029
50.0%
21708
21.2%
31246
 
15.5%
1707
 
8.8%
4306
 
3.8%
562
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.4029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12087
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4029
33.3%
04029
33.3%
21708
14.1%
31246
 
10.3%
1707
 
5.8%
4306
 
2.5%
562
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII12087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4029
33.3%
04029
33.3%
21708
14.1%
31246
 
10.3%
1707
 
5.8%
4306
 
2.5%
562
 
0.5%

sh308f
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing60
Missing (%)1.5%
Memory size238.0 KiB
2.0
1619 
3.0
1185 
1.0
552 
4.0
452 
5.0
212 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12060
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.01619
39.7%
3.01185
29.0%
1.0552
 
13.5%
4.0452
 
11.1%
5.0212
 
5.2%
(Missing)60
 
1.5%

Length

2021-09-07T20:35:41.786142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:35:41.853734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.01619
40.3%
3.01185
29.5%
1.0552
 
13.7%
4.0452
 
11.2%
5.0212
 
5.3%

Most occurring characters

ValueCountFrequency (%)
.4020
33.3%
04020
33.3%
21619
13.4%
31185
 
9.8%
1552
 
4.6%
4452
 
3.7%
5212
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8040
66.7%
Other Punctuation4020
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04020
50.0%
21619
20.1%
31185
 
14.7%
1552
 
6.9%
4452
 
5.6%
5212
 
2.6%
Other Punctuation
ValueCountFrequency (%)
.4020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4020
33.3%
04020
33.3%
21619
13.4%
31185
 
9.8%
1552
 
4.6%
4452
 
3.7%
5212
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII12060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4020
33.3%
04020
33.3%
21619
13.4%
31185
 
9.8%
1552
 
4.6%
4452
 
3.7%
5212
 
1.8%

Interactions

2021-09-07T20:35:09.067081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.171741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.275799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.375206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.474482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.577854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.685644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.795704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:09.901923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.007228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.115550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.220562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.325046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.426739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.535591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.645143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.753714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.866931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:10.975719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.086837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.198029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.310968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.426274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.542322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.655400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.773152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.881391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:11.988906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.101285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.217625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.335269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.438189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.545112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.645372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.745533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.850416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:12.956433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.067808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.179416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.283797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.391741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.492900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.596009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.695087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.802694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:13.912476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.011611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.117310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.219085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.318927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.420599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.525751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.634424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.739264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.840616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:14.947733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.177845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.285981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.385623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.491569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.611131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.718006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.828485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:16.933993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.039189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.146964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.256432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.366977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.474440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.583500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.693648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.796903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:17.898100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.007221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.127966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.251947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.365300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.488015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.601914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.709616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.823626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:18.943712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.067646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.192451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.307385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.428061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.541399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.652092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.763497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.877501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:19.993363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.107672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.229414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.339695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.452909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.583616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.698679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.819697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:20.935324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.050375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.177719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.289993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.399240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.509176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.626717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.744289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.850643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:21.967545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.075021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.182882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.291535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.405025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.520616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.634686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.743695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.859486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:22.967878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.072443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.180726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.292124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.406832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.508403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.617534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.720815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.823835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:23.928808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.037132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.150086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.259882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.364814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.474888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.577624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.679408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.783173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.889950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:24.998874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.109955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.226796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.335138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.444955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.556437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.674891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.793594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:25.907816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.021581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.140510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.251836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.358108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.467621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.590861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.712097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.813151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:26.918637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.019803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.119903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.223044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.329564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.442498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.547565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.657013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.765587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.867281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:27.968855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.070601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.182347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.293784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.391010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.494687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.592866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.691562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.791298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:28.893216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.000490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.104105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.205531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.309835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.406501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.501205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.597842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.702178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.809703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:29.909399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.015366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.116107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.217622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.321059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.426652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.537126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.645001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.750568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.859777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:30.959716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.057321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.165659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.267479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.366366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.472987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.586551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.695792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.803114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:31.913379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.024439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.137308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.250528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.358410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.473372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.581701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.689019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.787199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.885998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:32.989752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.101532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.219391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.332612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.443061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.556816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.670805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.789329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:33.904823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:34.017030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:34.132693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:34.247221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:34.357360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:34.457523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:34.560238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-07T20:35:41.968403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-07T20:35:42.214834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-07T20:35:42.461905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-07T20:35:42.706453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-07T20:35:42.917393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-07T20:35:34.804768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-07T20:35:35.246178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-07T20:35:35.560410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-07T20:35:35.965796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

supinepslpeffpslpprdptimeremptimes34ptimest1ptimest2pwasordi3pai_allavgsatminsatgenderraceage_s1saqlifosqsh308ash308bsh308csh308dsh308esh308f
067.072.1382.023.05.06.067.099.008.9518.6994.089.011416.1411.004.03.02.02.02.02.0
131.086.9381.028.01.04.066.027.0017.9515.5995.087.01154NaNNaN3.04.04.02.02.02.0
2100.080.5390.016.022.08.054.080.0025.6933.3892.073.023564.799.583.03.02.05.03.02.0
329.078.0400.026.012.010.052.092.0012.3030.0091.084.011546.8612.181.01.01.02.02.02.0
424.093.2433.028.05.06.061.020.002.225.5495.088.021405.2911.104.04.03.03.02.03.0
55.090.7422.017.024.06.053.038.002.8410.9594.088.011406.5711.682.02.02.02.01.03.0
6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1154NaNNaN1.01.03.02.01.05.0
7NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2351NaNNaN5.04.04.02.03.03.0
853.050.1186.015.00.014.071.0128.6217.4222.9096.091.011685.008.133.02.02.03.04.02.0
953.094.0445.030.00.011.059.026.003.7810.7995.090.011446.5012.582.02.03.02.02.03.0

Last rows

supinepslpeffpslpprdptimeremptimes34ptimest1ptimest2pwasordi3pai_allavgsatminsatgenderraceage_s1saqlifosqsh308ash308bsh308csh308dsh308esh308f
4070NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2156NaNNaN4.04.05.05.04.04.0
4071NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2165NaNNaN3.03.02.02.02.03.0
4072NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2155NaNNaN2.03.02.03.03.03.0
407369.072.4314.024.027.02.047.089.045.8642.8091.081.02171NaNNaN3.03.04.03.03.03.0
407437.087.8405.030.023.03.044.056.05.938.7495.087.02155NaNNaN3.04.04.02.03.03.0
407569.061.5400.025.05.03.066.0122.012.4518.3094.085.01159NaNNaN1.02.02.01.02.02.0
4076NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2154NaNNaN2.02.04.03.01.01.0
407764.078.0417.014.05.017.064.0118.054.5351.8095.081.01166NaNNaN3.03.02.02.01.05.0
4078NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1154NaNNaN3.03.02.01.01.02.0
4079NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1155NaNNaN2.03.05.02.03.03.0